72 research outputs found
Modeling Efficient Classification as a Process of Confidence Assessment and Delegation
In visual object detection and recognition, classifiers have two interesting characteristics: accuracy and speed. Accuracy depends on the complexity of the image features and classifier decision surfaces. Speed depends on the hardware and the computational effort required to use the features and decision surfaces. When attempts to increase accuracy lead to increases in complexity and effort, it is necessary to ask how much are we willing to pay for increased accuracy. For example, if increased computational effort implies quickly diminishing returns in accuracy, then those designing inexpensive surveillance applications cannot aim for maximum accuracy at any cost. It becomes necessary to find trade-offs between accuracy and effort.
We study efficient classification of images depicting real-world objects and scenes. Classification is efficient when a classifier can be controlled so that the desired trade-off between accuracy and effort (speed) is achieved and unnecessary computations are avoided on a per input basis. A framework is proposed for understanding and modeling efficient classification of images. Classification is modeled as a tree-like process. In designing the framework, it is important to recognize what is essential and to avoid structures that are narrow in applicability. Earlier frameworks are lacking in this regard.
The overall contribution is two-fold. First, the framework is presented, subjected to experiments, and shown to be satisfactory. Second, certain unconventional approaches are experimented with. This allows the separation of the essential from the conventional. To determine if the framework is satisfactory, three categories of questions are identified: trade-off optimization, classifier tree organization, and rules for delegation and confidence modeling. Questions and problems related to each category are addressed and empirical results are presented. For example, related to trade-off optimization, we address the problem of computational bottlenecks that limit the range of trade-offs. We also ask if accuracy versus effort trade-offs can be controlled after training. For another example, regarding classifier tree organization, we first consider the task of organizing a tree in a problem-specific manner. We then ask if problem-specific organization is necessary.Tässä työssä käsitellään kuvien automaattista luokittelua tehokkuuden näkökulmasta. Luokittelulla tarkoitetaan sitä, että kuville annetaan otsikoita ennalta sovitusta otsikoiden joukosta. Esimerkiksi kasvojen etsinnässä kuvia voidaan luokitella kasvokuviksi tai taustakuviksi.
Luokittelussa käytettävillä ohjelmilla, eli luokittelijoilla, on kaksi mielenkiintoista ominaisuutta: tarkkuus ja nopeus. Tarkkuudella tarkoitetaan todennäköisyyttä ennustaa kuvan luokka oikein. Tarkkuus riippuu kuvista etsittävien piirteiden ja luokittelijan käyttämien päätössääntöjen monimutkaisuudesta. Nopeus puolestaan riippuu käytettävästä laitteistosta ja luokittelijan laskennallisesta vaativuudesta.
Kun tarkkuuden kasvattaminen johtaa monimutkaisuuden ja laskennallisen vaativuuden kasvuun, on tarpeen harkita johtaako muutos haluttuun lopputulokseen. Jos esimerkiksi vaativuuden annetaan kasvaa merkittävästi, mutta tarkkuus paranee vain vähän, niin ei ole mahdollista tavoitella mahdollisimman tarkkoja ja samalla halpoja sovelluksia. Tällöin tarvitaan hallittuja vaihtokauppoja tarkkuuden ja nopeuden välillä.
Tässä työssä luokittelua sanotaan tehokkaaksi silloin, kun luokittelija voidaan säätää saavuttamaan haluttu vaihtokauppa tarkkuuden ja nopeuden välillä. Työssä ehdotetaan tiettyä mallinnuskehystä tehokkaan luokittelun mallintamiseksi ja ymmärtämiseksi. Luokittelua mallinnetaan puun kaltaisena prosessina, jossa kuvat kulkeutuvat juurisolmusta lehtisolmuihin päin. Puun säädettävistä parametreista riippuu kuinka syvälle puuhun kuvat kulkeutuvat. Syvyys on eräs nopeuteen ja tarkkuuteen vaikuttavista tekijöistä. Puun rakenne voi mukailla esimerkiksi luokkien hierarkiaa.
Työn kokonaiskontribuutio on kaksitahoinen. Ensiksi mallinnuskehys esitetään ja osoitetaan kokeellisesti tyydyttäväksi. Toiseksi työssä kokeillaan tiettyjä epätavallisia lähestymistapoja osaongelmiin. Jälkimmäisen takia saadaan selville mitä mallinnuskehyksessä tarvitaan ja mitä ei. Työssä tarkastellaan muun muossa laskennallisten pullonkaulojen muodostumista puihin, vaihtokauppoihin vaikuttavien parametrien säätöä luokittelijan koulutuksen jälkeen, sekä puun rakenteen muodostamiseen liittyviä kysymyksiä
Heuristic methods for support vector machines with applications to drug discovery.
The contributions to computer science presented in this thesis were inspired by the analysis of the data generated in the early stages of drug discovery. These data sets are generated by screening compounds against various biological receptors. This gives a first indication of biological activity. To avoid screening inactive compounds, decision rules for selecting compounds are required. Such a decision rule is a mapping from a compound representation to an estimated activity. Hand-coding such rules is time-consuming, expensive and subjective. An alternative is to learn these rules from the available data. This is difficult since the compounds may be characterized by tens to thousands of physical, chemical, and structural descriptors and it is not known which are most relevant to the prediction of biological activity. Further, the activity measurements are noisy, so the data can be misleading. The support vector machine (SVM) is a statistically well-founded learning machine that is not adversely affected by high-dimensional representations and is robust with respect to measurement inaccuracies. It thus appears to be ideally suited to the analysis of screening data. The novel application of the SVM to this domain highlights some shortcomings with the vanilla SVM. Three heuristics are developed to overcome these deficiencies: a stopping criterion, HERMES, that allows good solutions to be found in less time; an automated method, LAIKA, for tuning the Gaussian kernel SVM; and, an algorithm, STAR, that outputs a more compact solution. These heuristics achieve their aims on public domain data and are broadly successful when applied to the drug discovery data. The heuristics and associated data analysis are thus of benefit to both pharmacology and computer science
Remote Sensing
This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas
Statistical Data Modeling and Machine Learning with Applications
The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties
Tactile Perception And Visuotactile Integration For Robotic Exploration
As the close perceptual sibling of vision, the sense of touch has historically received less than deserved attention in both human psychology and robotics. In robotics, this may be attributed to at least two reasons. First, it suffers from the vicious cycle of immature sensor technology, which causes industry demand to be low, and then there is even less incentive to make existing sensors in research labs easy to manufacture and marketable. Second, the situation stems from a fear of making contact with the environment, avoided in every way so that visually perceived states do not change before a carefully estimated and ballistically executed physical interaction. Fortunately, the latter viewpoint is starting to change. Work in interactive perception and contact-rich manipulation are on the rise. Good reasons are steering the manipulation and locomotion communities’ attention towards deliberate physical interaction with the environment prior to, during, and after a task.
We approach the problem of perception prior to manipulation, using the sense of touch, for the purpose of understanding the surroundings of an autonomous robot. The overwhelming majority of work in perception for manipulation is based on vision. While vision is a fast and global modality, it is insufficient as the sole modality, especially in environments where the ambient light or the objects therein do not lend themselves to vision, such as in darkness, smoky or dusty rooms in search and rescue, underwater, transparent and reflective objects, and retrieving items inside a bag. Even in normal lighting conditions, during a manipulation task, the target object and fingers are usually occluded from view by the gripper. Moreover, vision-based grasp planners, typically trained in simulation, often make errors that cannot be foreseen until contact. As a step towards addressing these problems, we present first a global shape-based feature descriptor for object recognition using non-prehensile tactile probing alone. Then, we investigate in making the tactile modality, local and slow by nature, more efficient for the task by predicting the most cost-effective moves using active exploration. To combine the local and physical advantages of touch and the fast and global advantages of vision, we propose and evaluate a learning-based method for visuotactile integration for grasping
Advancements in the Industrial Internet of Things for Energy Efficiency
The Internet of Things is an emerging field that leverages the connections of everyday objects for the betterment of society. A subfield of this trend, the Industrial Internet of Things (IIoT), has been referred to as an industrial revolution that enhances both productivity and safety in the industrial environment. While still in its early stages, identified improvements have the potential to markedly improve manufacturing productivity.
Energy efficiency within manufacturing plants has traditionally received little focus. The Industrial Assessment Center Program demonstrates the potential energy improvements that can be realized in manufacturing plants, but these assessments also highlight some of the traditional barriers to energy efficiency. Some of these barriers include the lack of data to justify actionable improvements, unclear correlations between improvement costs and potential cost savings, and lack of knowledge on how energy improvements provide ancillary benefits to the plant. The IIoT has the potential to increase energy efficiency implementation in manufacturing plants by addressing these challenges.
This dissertation discusses the framework in which energy efficiency enhancements within the IIoT environment can be realized. The dissertation initially details the potential benefits of IIoT for energy efficiency and presents a general framework for these improvements. While proposed IIoT frameworks vary, they all include the core elements of improved sensing capabilities, enhanced data analysis, and
intelligent actuation. In addition to presenting the framework generally, the dissertation provides detailed case studies on how each of these framework elements lead to improved energy efficiency in manufacturing.
The first case study demonstrates improved sensing capabilities in the IIoT framework. A non-intrusive flow meter for use in compressed air and other fluid systems is presented. The second case study discusses Autonomous Robotic Assessments of Energy, which use advanced data analysis to autonomously perform a lighting energy assessment in facilities. The third case study is then presented on intelligent actuation, which uses a novel k-means algorithm to autonomously determine appropriate times to actuate compressors for air systems in manufacturing plants. Each of the presented case studies includes experimental tests demonstrating their capabilities
Sensors Fault Diagnosis Trends and Applications
Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis
Machine Learning in Sensors and Imaging
Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens
Human-Centric Machine Vision
Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans
Controlling realism and uncertainty in reservoir models using intelligent sedimentological prior information
Forecasting reservoir production has a large associated uncertainty, since this is the final part of a very complex process, this process is based on sparse and indirect data measurements. One the methodologies used in the oil industry to predict reservoir production is based on the Baye’s theorem. Baye’s theorem applied to reservoir forecasting, samples parameters from a prior understanding of the uncertainty to generate reservoir models and updates this prior information by comparing reservoir production data with model production response.
In automatic history matching it is challenging to generate reservoir models that preserve geological realism (obtain reservoir models with geological features that have been seen in nature). One way to control the geological realism in reservoir models is by controlling the realism of the geological prior information.
The aim of this thesis is to encapsulate sedimentological information in order to build prior information that can control the geological realism of the history-matched models. This “intelligent” prior information is introduced into the automatic history-matching framework rejecting geologically unrealistic reservoir models. Machine Learning Techniques (MLT) were used to build realistic sedimentological prior information models.
Another goal of this thesis was to include geological parameters into the automatic history-match framework that have an impact on reservoir model performance: vertical variation of facies proportions, connectivity of geobodies, and the use of multiple training images as a source of realistic sedimentological prior information.
The main outcome of this thesis is that the use of “intelligent” sedimentological prior information guarantees the realism of reservoir models and reduces computing time and uncertainty in reservoir production prediction
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